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Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

Electrode integration significantly increases the versatility of droplet microfluidics, enabling label-free sensing and manipulation at a single-droplet (single-cell) resolution. However, common fabrication techniques for integrating electronics into microfluidics are expensive, time-consuming, and can require cleanroom facilities. Here, we present a simple and cost-effective method for integrating electrodes into thermoplastic microfluidic chips using an off-the-shelf conductive ink. The developed conductive ink electrodes cost less than $10 for an entire chip, have been shown here in channel geometries as small as 75 μm by 50 μm, and can go from fabrication to testing within a day without a cleanroom. The geometric fabrication limits of this technique were explored over time, and proof-of-concept microfluidic devices for capacitance sensing, droplet merging, and droplet sorting were developed. This novel method complements existing rapid prototyping systems for microfluidics such as micromilling, laser cutting, and 3D printing, enabling their wider use and application.

Background In protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. Results We demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR’s performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. Conclusions The results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

In 2019, the first cases of SARS-CoV-2 were detected in Wuhan, China, and by early 2020 the cases were identified in the United States. SARS-CoV-2 infections increased in the US causing many states to implement stay-at-home orders and additional safety precautions to mitigate potential outbreaks. As policies changed throughout the pandemic and restrictions lifted, there was an increase in demand for Covid-19 testing which was costly, difficult to obtain, or had long turn-around times. Some academic institutions, including Boston University, created an on-campus Covid-19 screening protocol as part of planning for the safe return of students, faculty, and staff to campus with the option for in-person classes. At BU, we stood up an automated high-throughput clinical testing lab with the capacity to run 45,000 individual tests weekly by fall of 2020, with a purpose-built clinical testing laboratory, a multiplexed RT-PCR test, robotic instrumentation, and trained CLIA certified staff. There were challenges to overcome, including the supply chain issues for PPE testing materials, and equipment that were in high demand. The Boston University Clinical Testing Laboratory was operational at the start of the fall 2020 academic year. The lab performed over 1 million SARS-CoV-2 RT-PCR tests during the 2020-2021 academic year.

Key Points Question Can a multifaceted approach lead to control of COVID-19 transmission and spread on an urban campus? Findings In this case series including more than 500 000 COVID-19 tests and 719 individuals with COVID-19 at Boston University, active surveillance of campus populations, isolation of individuals with SARS-CoV-2 infection, early and vigorous contact tracing and quarantine, regular communication, robust data systems, and strong leadership were associated with minimal transmission of SARS-CoV-2. Most transmission occurred off campus, and there was no evidence of classroom transmission. Meaning These findings suggest that using frequent testing, vigorous contact tracing, rapid isolation and quarantine, and a strong leadership structure to ensure rapid decision-making and adaption to emerging data, controlling the spread of SARS-CoV-2 on an urban campus was feasible despite worsening local transmission during the semester., This case series describes the use of a multifaceted COVID-19 control plan to reduce spread of SARS-CoV-2 at a large urban university during the second wave of the pandemic., Importance The COVID-19 pandemic has severely disrupted US educational institutions. Given potential adverse financial and psychosocial effects of campus closures, many institutions developed strategies to reopen campuses in the fall 2020 semester despite the ongoing threat of COVID-19. However, many institutions opted to have limited campus reopening to minimize potential risk of spread of SARS-CoV-2. Objective To analyze how Boston University (BU) fully reopened its campus in the fall of 2020 and controlled COVID-19 transmission despite worsening transmission in Boston, Massachusetts. Design, Setting, and Participants This multifaceted intervention case series was conducted at a large urban university campus in Boston, Massachusetts, during the fall 2020 semester. The BU response included a high-throughput SARS-CoV-2 polymerase chain reaction testing facility with capacity to deliver results in less than 24 hours; routine asymptomatic screening for COVID-19; daily health attestations; adherence monitoring and feedback; robust contact tracing, quarantine, and isolation in on-campus facilities; face mask use; enhanced hand hygiene; social distancing recommendations; dedensification of classrooms and public places; and enhancement of all building air systems. Data were analyzed from December 20, 2020, to January 31, 2021. Main Outcomes and Measures SARS-CoV-2 diagnosis confirmed by reverse transcription–polymerase chain reaction of anterior nares specimens and sources of transmission, as determined through contact tracing. Results Between August and December 2020, BU conducted more than 500 000 COVID-19 tests and identified 719 individuals with COVID-19, including 496 students (69.0%), 11 faculty (1.5%), and 212 staff (29.5%). Overall, 718 individuals, or 1.8% of the BU community, had test results positive for SARS-CoV-2. Of 837 close contacts traced, 86 individuals (10.3%) had test results positive for COVID-19. BU contact tracers identified a source of transmission for 370 individuals (51.5%), with 206 individuals (55.7%) identifying a non-BU source. Among 5 faculty and 84 staff with SARS-CoV-2 with a known source of infection, most reported a transmission source outside of BU (all 5 faculty members [100%] and 67 staff members [79.8%]). A BU source was identified by 108 of 183 undergraduate students with SARS-CoV-2 (59.0%) and 39 of 98 graduate students with SARS-CoV-2 (39.8%); notably, no transmission was traced to a classroom setting. Conclusions and Relevance In this case series of COVID-19 transmission, BU used a coordinated strategy of testing, contact tracing, isolation, and quarantine, with robust management and oversight, to control COVID-19 transmission in an urban university setting.

Most digital electronic circuits utilize a timing reference to synchronize the progression of signals and enable sequential memory elements. These designs may not be realizable in biological substrates due to the lack of a reliable high-frequency clock signal. Asynchronous designs eliminate the need for a clock with data encodings and request/acknowledge handshake protocols. This paper proposes a workflow to automate the design of asynchronous genetic circuits. This workflow extends genetic design tools by leveraging asynchronous logic design methods customized for this technology. This workflow is demonstrated on a genetic sensor that uses filtering and cellular communication to improve its reliability.

The design of microfluidic Lab on a Chip (LoC) systems is an onerous task requiring specialized skills in fluid dynamics, mechanical design drafting, and manufacturing. Engineers face significant challenges during the labor-intensive process of designing microfluidic devices, with very few specialized tools that help automate the process. Typical design iterations require the engineer to research the architecture, manually draft the device layout, optimize for manufacturing processes, and manually calculate and program the valve sequences that operate the microfluidic device. The problem compounds when engineers not only have to test the functionality of the chip but are also expected to optimize them for the robust execution of biological assays. In this paper, we present an interactive tool for designing continuous flow microfluidic devices. 3DμF is the first completely open source interactive microfluidic system designer that readily supports state of the art design automation algorithms. Through various case studies, we show 3DμF can be used to reproduce designs from literature, provide metrics for evaluating microfluidic design complexity and showcase how 3DμF is a platform for integrating a wide assortment of engineering techniques used in the design of microfluidic devices as a part of the standard design workflow.

The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiplexed, high-throughput fashion, allowing many different configurations to be tested simultaneously. However, metrics are required to determine when automation is warranted based on factors such as assembly methodology, protocol details, and number of samples. The goal of our synthetic biology automation work is to develop and test protocols, hardware, and software to investigate and optimize DNA assembly through quantifiable metrics. We performed a parameter analysis of DNA assembly to develop a standardized, highly efficient, and reproducible Modular Cloning protocol, suitable to be used both manually, and with liquid-handling robots. We created a key DNA assembly metric (Q-metric) to characterize a given automation method’s advantages over conventional manual manipulations with regards to researchers’ highest-priority parameters: output, cost, and time. A software tool called Puppeteer was developed to formally capture these metrics, help define the assembly design, and provide human and robotic liquid handling instructions. Altogether, we contribute to a growing foundation of standardizing practices, metrics, and protocols for automating DNA assembly.

ImportanceThe coronavirus disease 2019 (COVID-19) pandemic has severely disrupted United States educational institutions. Given potential adverse financial psychosocial effects of campus closures, many institutions developed strategies to reopen campuses in the fall despite the ongoing threat of COVID-19. Many however opted to have limited campus re-opening in order to minimize potential risk of spread of SARS-CoV-2.ObjectiveTo analyze how Boston University (BU) fully reopened its campus in the fall of 2020 and controlled COVID-19 transmission despite worsening transmission in the city of Boston.DesignMulti-faceted intervention case study.SettingLarge urban university campus.InterventionsThe BU response included a high-throughput SARS-CoV-2 PCR testing facility with capacity to delivery results in less than 24 hours; routine asymptomatic screening for COVID-19; daily health attestations; compliance monitoring and feedback; robust contact tracing, quarantine and isolation in on campus facilities; face mask use; enhanced hand hygiene; social distancing recommendations; de-densification of classrooms and public places; and enhancement of all building air systems.Main Outcomes and MeasuresBetween August and December 2020, BU conducted >500,000 COVID-19 tests and identified 719 individuals with COVID-19: 496 (69.0%) students, 11 (1.5%) faculty, and 212 (29.5%) staff. Overall, about 1.8% of the BU community tested positive. Of 837 close contacts traced, 86 (10.3%) tested positive for COVID-19. BU contact tracers identified a source of transmission for 51.5% of cases with 55.7% identifying a source outside of BU. Among infected faculty and staff with a known source of infection, the majority reported a transmission source outside of BU (100% for faculty and 79.8% for staff). A BU source was identified by 59.2% of undergraduate students and 39.8% of graduate students; notably no transmission was traced to a classroom setting.Conclusions and RelevanceBU was successful in containing COVID-19 transmission on campus while minimizing off campus acquisition of COVID-19 from the greater Boston area. A coordinated strategy of testing, contact tracing, isolation and quarantine, with robust management and oversight, can control COVID-19 transmission, even in an urban university setting.Key PointsQuestionCan a multi-faceted approach lead to control of COVID-19 transmission and spread on an urban campus?FindingsDespite a second wave of SARS-CoV-2 in the greater Boston area, Boston University was able to minimize outbreaks by means of active surveillance of campus populations, isolation of infected individuals, early, effective contact tracing and quarantine, regular communication, excellent data systems, and strong leadership. Most transmission appeared to occur off campus and there was no evidence of classroom transmission.MeaningUsing the main axioms of infection control including frequent testing, vigorous contact tracing, and rapid isolation and quarantine, and a strong leadership structure to ensure nimble decision-making and rapid adaption to emerging data, controlling the transmission and spread of SARS-CoV-2 on an urban campus was feasible despite worsening local transmission during the course of the semester.

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences., Devices for droplet generation are at the heart of many microfluidic applications but difficult to tailor for specific cases. Lashkaripour et al. show how design customization can greatly be simplified by combining rapid prototyping with data-driven machine learning strategies.

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through., Vaidyanathan et al. present a heuristic algorithm for the selection of fluorescent reporters in the context of single-cell analysis. They present a tool to enable biologists to design multi-colour fluorophore panels based on specific equipment’s configurations. The authors demonstrate the efficacy of their algorithm by comparing computational predictions with experimental observations.

Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.

There was an explosion in the amount of commercially available DNA in sequence repositories over the last decade. The number of such plasmids increased from 12,000 to over 300,000 among three of the largest repositories: iGEM, Addgene, and DNASU. A challenge in biodesign remains how to use these and other repository-based sequences effectively, correctly, and seamlessly. This work describes an approach to plasmid design where a plasmid is specified as simply a DNA sequence or list of features. The proposed software then finds the most cost-effective combination of synthetic and PCR-prepared repository fragments to build the plasmid via Gibson assembly®. It finds existing DNA sequences in both user-specified and public DNA databases: iGEM, Addgene, and DNASU. Such a software application is introduced and characterized against all post-2005 iGEM composite parts and all Addgene vectors submitted in 2018 and found to reduce costs by 34% versus a purely synthetic plasmid design approach. The described software will improve current plasmid assembly workflows by shortening design times, improving build quality, and reducing costs.

Synthetic mRNA is an attractive vehicle for gene therapies because of its transient nature and improved safety profile over DNA. However, unlike DNA, broadly applicable methods to control expression from mRNA are lacking. Here we describe a platform for small-molecule-based regulation of expression from modified RNA (modRNA) and self-replicating RNA (replicon) delivered to mammalian cells. Specifically, we engineer small-molecule-responsive RNA binding proteins to control expression of proteins from RNA-encoded genetic circuits. Coupled with specific modRNA dosages or engineered elements from a replicon, including a subgenomic promoter library, we demonstrate the capability to externally regulate the timing and level of protein expression. These control mechanisms facilitate the construction of ON, OFF, and two-output switches, with potential therapeutic applications such as inducible cancer immunotherapies. These circuits, along with other synthetic networks that can be developed using these tools, will expand the utility of synthetic mRNA as a therapeutic modality.

Droplet-based microfluidics is capable of being a superior platform for biological and biomedical applications due to their higher accuracy, throughput and sensitivity, low reagent consumption and fast reaction time. However, the complex and nonlinear behavior of multiphase microfluidic devices and several parameters affecting droplet size simultaneously necessitates costly design iterations to generate droplets with the radius of interest for a certain application. To address this, we exploit soft computing methods to bridge fuzzy systems and neural networks in order to build an adaptive neural-fuzzy inference system (ANFIS) that predicts the droplet size generated in a flow-focusing microfluidic device based on six major parameters, that includes geometry, flow, and fluid properties. This model shows a significant accuracy with a coefficient of determination of 0.96 when compared to the observed data points. Once the ANFIS model is built and verified, we use it to study the effect of each input parameter on droplet size which is challenging and/or expensive to determine experimentally.

We present a formal language for specifying via constraints a "design space" of DNA constructs composed of genetic parts, and an algorithm for automatically and correctly creating a novel representation of the space of satisfying designs. The language is simple, captures a large class of design spaces, and possesses algorithms for common operations on design spaces. The flexibility of this approach is demonstrated using a 16-gene nitrogen fixation pathway and genetic logic circuits.

The lack of standardization in the specification and representation of microfluidic designs and their corresponding architectures is one of the largest hurdles faced by the developers of Microfluidic Design Automation (MDA) tools. In this paper, we introduce MINT, a Microfluidic Hardware Description Language (MHDL) for defining components and devices in a human readable manner, and ParchMint, an MDA interchange format and associated benchmark suite that can be used to compare the performance of different physical design algorithms. We further demonstrate how the introduction of MINT and ParchMint into the engineering workflow can bridge the gaps from the specification to the fabrication of microfluidic devices. While recent efforts to democratize microfluidics have been recognized by the community, there is an unfortunate lack of open source tools, design languages, and standards. Consequently, microfluidic designs shared on open platforms such as Metafluidics[15] leave conceptual gaps in terms of missing design information that are necessary to realize the “creative process flows” (Reproduce, Remix, and Test multiple systems). MINT and ParchMint are open source projects, which allows the community to contribute and extend their functionality to enable advanced algorithmic methodologies and new commercialization possibilities that differ from the vertically integrated industries we see today.

There was an explosion in the amount of commercially available DNA in sequence repositories over the last decade. The number of such plasmids increased from 12,000 to over 300,000 among three of the largest repositories: iGEM, Addgene, and DNASU. The challenge in biodesign remains how to use these and other repository-based sequences effectively, correctly, and seamlessly. This work describes an approach to plasmid design where a plasmid is specified as a DNA sequence or list of features. The proposed software then finds the most cost-effective combination of synthetic and PCR-prepared repository fragments to build the plasmid via Gibson Assembly. It finds existing DNA sequences in user-specified and public DNA databases: iGEM, Addgene, and DNASU. Such a software application is introduced and characterized against all post-2005 iGEM composite parts and all Addgene vectors submitted in 2018 and found to reduce costs by 34% versus a purely synthetic plasmid design approach. The described software will improve current plasmid assembly workflows by shortening design times, improving build quality, and reducing costs.

The required step in all droplet-based devices is droplet formation. A droplet generator must deliver an application-specific performance that includes a prescribed droplet size and generation frequency while producing monodisperse droplets. The desired performance is usually reached through several cost- and time-inefficient design iterations. To address this, we take advantage of a low-cost rapid prototyping method and provide a framework that enables researchers to make informed decisions on how to change geometric parameters and flow conditions to tune the performance of a microfluidic flow-focusing droplet generator. We present the primary and secondary parameters necessary for fine-tuning droplet formation over a wide range of capillary numbers and flow rate ratios. Once the key parameters are identified, we demonstrate the effect of geometric parameters and flow conditions on droplet size, generation rate, polydispersity, and generation regime. Using this framework, a wide range of droplet diameters (i.e., 30-400 μm) and generation rates (i.e., 0.5-800 Hz) was achieved.

The Synthetic Biology Open Language (SBOL) is a standard that enables collaborative engineering of biological systems across different institutions and tools. SBOL is developed through careful consideration of recent synthetic biology trends, real use cases, and consensus among leading researchers in the field and members of commercial biotechnology enterprises. We demonstrate and discuss how a set of SBOL-enabled software tools can form an integrated, cross-organizational workflow to recapitulate the design of one of the largest published genetic circuits to date, a 4-input AND sensor. This design encompasses the structural components of the system, such as its DNA, RNA, small molecules, and proteins, as well as the interactions between these components that determine the system's behavior/function. The demonstrated workflow and resulting circuit design illustrate the utility of SBOL 2.0 in automating the exchange of structural and functional specifications for genetic parts, devices, and the biological systems in which they operate.

Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.

Microfluidic devices, by definition, are required to move liquids from one physical location to another. Given a finite and frequently fixed set of physical channels to route fluids, a primitive design element that allows reconfigurable routing of that fluid from any of n input ports to any n output ports will dramatically change the paradigms by which these chips are designed and applied. Furthermore, if these elements are "regular" regarding their design, the programming and fabrication of these elements becomes scalable. This paper presents such a design element called a transposer. We illustrate the design, fabrication and operation of a single transposer. We then scale this design to create a programmable fabric towards a general-purpose, reconfigurable microfluidic platform analogous to the Field Programmable Gate Array (FPGA) found in digital electronics.